Feature Markov Decision Processes
Date
2009
Authors
Hutter, Marcus
Journal Title
Journal ISSN
Volume Title
Publisher
Atlantis Press
Abstract
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). So far it is
Description
Keywords
Keywords: Dynamic Bayesian network; Finite state; General purpose; Intelligent learning; Markov Decision Processes; Objective criteria; State representation; Bayesian networks; Inference engines; Intelligent agents; Markov processes; Reinforcement; Learning algorit
Citation
Collections
Source
Advances in Intelligent Systems Research: Proceedings of the 2nd Conference on Artificial General Intelligence (AGI 2009)
Type
Conference paper
Book Title
Entity type
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Restricted until
2037-12-31